import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

__a    :	Dict         =    [
    # (stable-diffusion, HF Diffusers)
    ("""time_embed.0.weight""", """time_embedding.linear_1.weight"""),
    ("""time_embed.0.bias""", """time_embedding.linear_1.bias"""),
    ("""time_embed.2.weight""", """time_embedding.linear_2.weight"""),
    ("""time_embed.2.bias""", """time_embedding.linear_2.bias"""),
    ("""input_blocks.0.0.weight""", """conv_in.weight"""),
    ("""input_blocks.0.0.bias""", """conv_in.bias"""),
    ("""out.0.weight""", """conv_norm_out.weight"""),
    ("""out.0.bias""", """conv_norm_out.bias"""),
    ("""out.2.weight""", """conv_out.weight"""),
    ("""out.2.bias""", """conv_out.bias"""),
]

__a    :	str         =    [
    # (stable-diffusion, HF Diffusers)
    ("""in_layers.0""", """norm1"""),
    ("""in_layers.2""", """conv1"""),
    ("""out_layers.0""", """norm2"""),
    ("""out_layers.3""", """conv2"""),
    ("""emb_layers.1""", """time_emb_proj"""),
    ("""skip_connection""", """conv_shortcut"""),
]

__a    :	int         =    []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
		# loop over downblocks/upblocks

		for j in range(2):
				# loop over resnets/attentions for downblocks
				__a    :	Union[str, Any]         =    F'''down_blocks.{i}.resnets.{j}.'''
				__a    :	Optional[Any]         =    F'''input_blocks.{3*i + j + 1}.0.'''
				unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

				if i < 3:
						# no attention layers in down_blocks.3
						__a    :	Tuple         =    F'''down_blocks.{i}.attentions.{j}.'''
						__a    :	Optional[Any]         =    F'''input_blocks.{3*i + j + 1}.1.'''
						unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

		for j in range(3):
				# loop over resnets/attentions for upblocks
				__a    :	Tuple         =    F'''up_blocks.{i}.resnets.{j}.'''
				__a    :	Optional[Any]         =    F'''output_blocks.{3*i + j}.0.'''
				unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

				if i > 0:
						# no attention layers in up_blocks.0
						__a    :	str         =    F'''up_blocks.{i}.attentions.{j}.'''
						__a    :	Any         =    F'''output_blocks.{3*i + j}.1.'''
						unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

		if i < 3:
				# no downsample in down_blocks.3
				__a    :	Union[str, Any]         =    F'''down_blocks.{i}.downsamplers.0.conv.'''
				__a    :	Dict         =    F'''input_blocks.{3*(i+1)}.0.op.'''
				unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

				# no upsample in up_blocks.3
				__a    :	List[Any]         =    F'''up_blocks.{i}.upsamplers.0.'''
				__a    :	str         =    F'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
				unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

__a    :	Union[str, Any]         =    """mid_block.attentions.0."""
__a    :	str         =    """middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
		__a    :	Dict         =    F'''mid_block.resnets.{j}.'''
		__a    :	Union[str, Any]         =    F'''middle_block.{2*j}.'''
		unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				{k: k for k in unet_state_dict.keys()}
				for sd_name, hf_name in unet_conversion_map:
								__lowercase					     =				sd_name
				for k, v in mapping.items():
								if "resnets" in k:
												for sd_part, hf_part in unet_conversion_map_resnet:
																__lowercase					     =				v.replace(lowercase				,     lowercase				)
												__lowercase					     =				v
				for k, v in mapping.items():
								for sd_part, hf_part in unet_conversion_map_layer:
												__lowercase					     =				v.replace(lowercase				,     lowercase				)
								__lowercase					     =				v
				__lowercase					     =				{v: unet_state_dict[k] for k, v in mapping.items()}
				return new_state_dict


# ================#
# VAE Conversion #
# ================#

__a    :	Any         =    [
    # (stable-diffusion, HF Diffusers)
    ("""nin_shortcut""", """conv_shortcut"""),
    ("""norm_out""", """conv_norm_out"""),
    ("""mid.attn_1.""", """mid_block.attentions.0."""),
]

for i in range(4):
		# down_blocks have two resnets
		for j in range(2):
				__a    :	Any         =    F'''encoder.down_blocks.{i}.resnets.{j}.'''
				__a    :	Optional[Any]         =    F'''encoder.down.{i}.block.{j}.'''
				vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

		if i < 3:
				__a    :	Any         =    F'''down_blocks.{i}.downsamplers.0.'''
				__a    :	int         =    F'''down.{i}.downsample.'''
				vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

				__a    :	Tuple         =    F'''up_blocks.{i}.upsamplers.0.'''
				__a    :	int         =    F'''up.{3-i}.upsample.'''
				vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

		# up_blocks have three resnets
		# also, up blocks in hf are numbered in reverse from sd
		for j in range(3):
				__a    :	Optional[int]         =    F'''decoder.up_blocks.{i}.resnets.{j}.'''
				__a    :	Optional[Any]         =    F'''decoder.up.{3-i}.block.{j}.'''
				vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
		__a    :	Optional[int]         =    F'''mid_block.resnets.{i}.'''
		__a    :	str         =    F'''mid.block_{i+1}.'''
		vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


__a    :	List[str]         =    [
    # (stable-diffusion, HF Diffusers)
    ("""norm.""", """group_norm."""),
    ("""q.""", """query."""),
    ("""k.""", """key."""),
    ("""v.""", """value."""),
    ("""proj_out.""", """proj_attn."""),
]





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				return w.reshape(*w.shape				,     1				,     1				)





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				{k: k for k in vae_state_dict.keys()}
				for k, v in mapping.items():
								for sd_part, hf_part in vae_conversion_map:
												__lowercase					     =				v.replace(lowercase				,     lowercase				)
								__lowercase					     =				v
				for k, v in mapping.items():
								if "attentions" in k:
												for sd_part, hf_part in vae_conversion_map_attn:
																__lowercase					     =				v.replace(lowercase				,     lowercase				)
												__lowercase					     =				v
				__lowercase					     =				{v: vae_state_dict[k] for k, v in mapping.items()}
				__lowercase					     =				['''q''', '''k''', '''v''', '''proj_out''']
				for k, v in new_state_dict.items():
								for weight_name in weights_to_convert:
												if F"mid.attn_1.{weight_name}.weight" in k:
																print(F"Reshaping {k} for SD format"				)
																__lowercase					     =				reshape_weight_for_sd(lowercase				)
				return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


__a    :	Any         =    [
    # (stable-diffusion, HF Diffusers)
    ("""resblocks.""", """text_model.encoder.layers."""),
    ("""ln_1""", """layer_norm1"""),
    ("""ln_2""", """layer_norm2"""),
    (""".c_fc.""", """.fc1."""),
    (""".c_proj.""", """.fc2."""),
    (""".attn""", """.self_attn"""),
    ("""ln_final.""", """transformer.text_model.final_layer_norm."""),
    ("""token_embedding.weight""", """transformer.text_model.embeddings.token_embedding.weight"""),
    ("""positional_embedding""", """transformer.text_model.embeddings.position_embedding.weight"""),
]
__a    :	Any         =    {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__a    :	Dict         =    re.compile("""|""".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
__a    :	Optional[Any]         =    {"""q""": 0, """k""": 1, """v""": 2}





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				{}
				__lowercase					     =				{}
				__lowercase					     =				{}
				for k, v in text_enc_dict.items():
								if (
								    k.endswith('''.self_attn.q_proj.weight'''				)
								    or k.endswith('''.self_attn.k_proj.weight'''				)
								    or k.endswith('''.self_attn.v_proj.weight'''				)
								):
												__lowercase					     =				k[: -len('''.q_proj.weight'''				)]
												__lowercase					     =				k[-len('''q_proj.weight'''				)]
												if k_pre not in capture_qkv_weight:
																__lowercase					     =				[None, None, None]
												__lowercase					     =				v
												continue

								if (
								    k.endswith('''.self_attn.q_proj.bias'''				)
								    or k.endswith('''.self_attn.k_proj.bias'''				)
								    or k.endswith('''.self_attn.v_proj.bias'''				)
								):
												__lowercase					     =				k[: -len('''.q_proj.bias'''				)]
												__lowercase					     =				k[-len('''q_proj.bias'''				)]
												if k_pre not in capture_qkv_bias:
																__lowercase					     =				[None, None, None]
												__lowercase					     =				v
												continue

								__lowercase					     =				textenc_pattern.sub(lambda lowercase				: protected[re.escape(m.group(0				)				)]				,     lowercase				)
								__lowercase					     =				v

				for k_pre, tensors in capture_qkv_weight.items():
								if None in tensors:
												raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''				)
								__lowercase					     =				textenc_pattern.sub(lambda lowercase				: protected[re.escape(m.group(0				)				)]				,     lowercase				)
								__lowercase					     =				torch.cat(lowercase				)

				for k_pre, tensors in capture_qkv_bias.items():
								if None in tensors:
												raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''				)
								__lowercase					     =				textenc_pattern.sub(lambda lowercase				: protected[re.escape(m.group(0				)				)]				,     lowercase				)
								__lowercase					     =				torch.cat(lowercase				)

				return new_state_dict





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				return text_enc_dict


if __name__ == "__main__":
		__a    :	Dict         =    argparse.ArgumentParser()

		parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
		parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
		parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
		parser.add_argument(
		    """--use_safetensors""", action="""store_true""", help="""Save weights use safetensors, default is ckpt."""
		)

		__a    :	Any         =    parser.parse_args()

		assert args.model_path is not None, "Must provide a model path!"

		assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

		# Path for safetensors
		__a    :	Any         =    osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
		__a    :	Dict         =    osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
		__a    :	str         =    osp.join(args.model_path, """text_encoder""", """model.safetensors""")

		# Load models from safetensors if it exists, if it doesn't pytorch
		if osp.exists(unet_path):
				__a    :	List[str]         =    load_file(unet_path, device="""cpu""")
		else:
				__a    :	List[str]         =    osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
				__a    :	List[Any]         =    torch.load(unet_path, map_location="""cpu""")

		if osp.exists(vae_path):
				__a    :	str         =    load_file(vae_path, device="""cpu""")
		else:
				__a    :	List[Any]         =    osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
				__a    :	Optional[int]         =    torch.load(vae_path, map_location="""cpu""")

		if osp.exists(text_enc_path):
				__a    :	List[str]         =    load_file(text_enc_path, device="""cpu""")
		else:
				__a    :	str         =    osp.join(args.model_path, """text_encoder""", """pytorch_model.bin""")
				__a    :	Optional[Any]         =    torch.load(text_enc_path, map_location="""cpu""")

		# Convert the UNet model
		__a    :	Any         =    convert_unet_state_dict(unet_state_dict)
		__a    :	List[Any]         =    {"""model.diffusion_model.""" + k: v for k, v in unet_state_dict.items()}

		# Convert the VAE model
		__a    :	Optional[Any]         =    convert_vae_state_dict(vae_state_dict)
		__a    :	str         =    {"""first_stage_model.""" + k: v for k, v in vae_state_dict.items()}

		# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
		__a    :	List[str]         =    """text_model.encoder.layers.22.layer_norm2.bias""" in text_enc_dict

		if is_vaa_model:
				# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
				__a    :	Optional[int]         =    {"""transformer.""" + k: v for k, v in text_enc_dict.items()}
				__a    :	str         =    convert_text_enc_state_dict_vaa(text_enc_dict)
				__a    :	List[Any]         =    {"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
		else:
				__a    :	Dict         =    convert_text_enc_state_dict(text_enc_dict)
				__a    :	int         =    {"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}

		# Put together new checkpoint
		__a    :	Any         =    {**unet_state_dict, **vae_state_dict, **text_enc_dict}
		if args.half:
				__a    :	List[Any]         =    {k: v.half() for k, v in state_dict.items()}

		if args.use_safetensors:
				save_file(state_dict, args.checkpoint_path)
		else:
				__a    :	Tuple         =    {"""state_dict""": state_dict}
				torch.save(state_dict, args.checkpoint_path)
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase					):


			"""simple docstring"""






			@register_to_config
			def __init__(  self ,  *,
			    lowerCAmelCase__ = 4 ,  lowerCAmelCase__ = 7_68 ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							super().__init__()

							__lowercase					     =				nn.Parameter(torch.zeros(lowerCAmelCase__		)		)

							# parameters for additional clip time embeddings
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)

							# parameters for encoder hidden states
							__lowercase					     =				clip_extra_context_tokens
							__lowercase					     =				nn.Linear(
							    lowerCAmelCase__ ,  self.clip_extra_context_tokens * cross_attention_dim		)
							__lowercase					     =				nn.Linear(lowerCAmelCase__ ,  lowerCAmelCase__		)
							__lowercase					     =				nn.LayerNorm(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  *, lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							if do_classifier_free_guidance:
											# Add the classifier free guidance embeddings to the image embeddings
											__lowercase					     =				image_embeddings.shape[0]
											__lowercase					     =				self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
											__lowercase					     =				classifier_free_guidance_embeddings.expand(
											    lowerCAmelCase__ ,  -1		)
											__lowercase					     =				torch.cat([classifier_free_guidance_embeddings, image_embeddings] ,  dim=0		)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							__lowercase					     =				prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							__lowercase					     =				self.embedding_proj(lowerCAmelCase__		)
							__lowercase					     =				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__		)
							__lowercase					     =				time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							__lowercase					     =				self.clip_extra_context_tokens_proj(lowerCAmelCase__		)
							__lowercase					     =				clip_extra_context_tokens.reshape(lowerCAmelCase__ ,  -1 ,  self.clip_extra_context_tokens		)
							__lowercase					     =				clip_extra_context_tokens.permute(0 ,  2 ,  1		)

							__lowercase					     =				self.encoder_hidden_states_proj(lowerCAmelCase__		)
							__lowercase					     =				self.text_encoder_hidden_states_norm(lowerCAmelCase__		)
							__lowercase					     =				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] ,  dim=1		)

							return text_encoder_hidden_states, additive_clip_time_embeddings